The COVID-19 era has witnessed numerous successful and unsuccessful attempts to adapt or reconfigure physical, virtual, and hybrid aspects of the built environment in order to mitigate the risks of co-occuring (i.e., compound) hazards. But it has also witnessed major challenges to ensuring that the protections these reconfigurations afford are equitably distributed. Additional theoretical and empirical research is needed to inform transitions (via adaptive reconfiguration) toward short-term goals of health and well-being, as well as to guide transformations (via the establishment of stable configuration) toward longer-term goals of equitable societal function. To this end, this paper presents a framework for conceptualizing adaptation of the built environment as a series of state transitions in response to (or in anticipation of) compound hazards. It draws upon cases from recent experience in the areas of food production, shelter, and education to critique, clarify, and explicate this framework. It concludes with implications for further research on the management of transitions in the built environment under a range of hazard scenarios. The COVID-19 pandemic provided a global backdrop for the study of the capacities and vulnerabilities of many aspects of societal function, challenging conventions around the design and operation of wide classes of infrastructure to protect populations from pandemics as well as hazards such as hurricanes and earthquakes. The framework offered by this study, and its application through the associated case studies, reveals how observed adaptations of the built environment can elucidate new potentials to mitigate the risks associated with co-occuring (i.e., compound) hazards, as well as areas where our existing conventions are no longer compatible with contemporary uses of the built environment. One such convention challenged by this study is the definition of critical infrastructure in existing regulatory frameworks. The built environment transitions documented by this study suggest that contemporary notions of what infrastructure is critical and what services are essential have outstripped the traditional notions in codes and standards, demanding corresponding realignment of regulatory frameworks to ensure life-safety can still be achieved as usages evolve.

Trade-offs - between risk and reward, efficiency and effectiveness - are endemic to an organisation's evolution and success. Until recently, however, organisational performance studies have suffered from a lack of detailed, longitudinal data, and therefore of methods that could exploit these data. While many organisations now deploy instrumentation to collect data on operations, those data are seldom directly suited to researchers' aims and are therefore characterised as 'secondary'. This paper addresses this two-fold gap by casting secondary data within a theoretically grounded measurement framework and employing an innovative approach - based on data envelopment analysis - to assessing the additional value provided by the data's temporal aspects. The domain of application, post-disaster debris removal, is time-constrained, potentially expensive, and crucial to post-disaster recovery. The results of this study strongly suggest the relevance of temporal aspects of the data to modelling of performance trade-offs, but also the need for further development of novel methodological approaches to examining performance trade-offs.

Extreme events such as large-scale natural disasters create the need for cooperation within and among responding organizations. Activities to mitigate the effects of these events can be expected to range from planned to improvised. This paper presents a methodology for describing both the context and substance of improvisation during the response phase. The context is described by (i) analyzing communication patterns among personnel in and among responding organizations and (ii) determining the appropriateness of existing plans to the event. The substance of improvisation within this context is described by modeling the behavior and cognition of response personnel. Application of the methodology leads to descriptions of improvisation and its context that may be stored in machine-readable format for use either by researchers, responding organizations or designers of computer-based tools to support improvised decision making. Data collection strategies for implementing the methodology are discussed and selected steps illustrated using a data set from a large-scale natural disaster.

Many important indicators of the inputs, mediators, and outcomes of team performance have remained stubbornly resistant to sustained and detailed measurement. Team-based video games, now played by untold millions of people worldwide, offer the potential to provide data to support the analysis of these and other team-centered phenomena; however, theoretically grounded guidance is needed to guide game selection and modification to support such analysis. Accordingly, this article explores prospects and challenges for using currently available open video games (that is, ones which are free, open source, and open architecture) to improve understanding of team performance. The approach combines systematic classification, structured critique, and close analysis to identify games with strong potential for explorations of teamwork by human or mixed human-agent teams. This article concludes with a discussion of promising trajectories for future research.

International Conference on Human-Machine Systems, held in 2020 (ICHMS 2020). ICHMS is intended to foster the development and dissemination of research for advancing the science and engineering of humanmachine system at multiple levels, including systems, computing technology, and human-automation interaction.The conference stresses methodological innovation, novel results and implications for practice in a broad range of application areas, including transportation and autonomous vehicle systems, the Internet of Things, smart communities, motorized cyber-physical systems, and human-robot interaction (HRI).ICHMS 2020 drew-in approximately 200 participants (despite the COVID-19 pandemic) and generated ~150 peer reviewed papers.The meeting was selective in its acceptance rate and the conference yielded a refereed proceedings publication.The quality of contributions to this meeting was such that many of the papers could have been considered for publication as regular papers in the IEEE Transactions on Human-Machine Systems (THMS) with unique extensions.This SI seeks high-quality and significant extensions of contributions to ICHMS 2021.THMS has not previously published regular papers as extensions of conference proceeding publications.For this reason, any manuscript submitted to the present SI, and ultimately published in THMS, will be required to present an extension of a research study presented at the conference of no less than 40% new results, inferences and conclusions for the scientific community.Submissions to the SI will align with the call for contributions to ICHMS 2021 and must also fall within the scope of the Journal.We welcome a broad range of contributions for advancing understanding of human-machine systems, whether through analysis, design, operations or other aspects.Potential contributions may address, but are not limited to, the following topics: companion technology; brain-machine interfaces and systems; human-artificial intelligence (AI) interaction; cognitive computing and engineering; interactive and wearable computing and devices; collaborative intelligence systems and applications; and human factors engineeringThe SI will be published over two issues of Transactions on Human-Machine Systems.The SI will serve as a forum for multidisciplinary and systematic research investigations presented at the 2 nd ICHMS.In specific, the publication is expected to reveal new guidelines for design and engineering of technology to support human information processing and decision making in advanced systems control.It is important to note that any submissions to the special issue must have been accepted to the 2 nd ICHMS in order to be considered by THMS for potential publication.THMS will consider both full regular papers and technical correspondences.

This study examines the performance of cognitive work—as constrained by physical, policy and resource-related factors—in the near-simultaneous design and execution of disaster response operations. The demands of the situation described here—the removal of debris from a high-value barrier island in the US state of New York after Hurricane Sandy (2012)—lay at the far boundaries of the responding organizations’ experience, making this case an excellent candidate for study. Data are analyzed on the deliberative processes of the responding organization in order to characterize the interaction between the design and operation of the debris removal network over time. Statistical data modeling of these processes reveals a number of temporal dependencies between ideation and decision-making processes, as well as between components of the system. The illumination and quantification of these processes using data produced through normal operations contributes significantly to theories of the cognitive and behavioral phenomena that underlie organizational response to highly non-routine events, thus building upon broader theories of organizational improvisation and performance.

is an organization within the framework of the IEEE, with professional interest in the closely interrelated fields of man-machine systems, systems science, systems engineering, and cybernetics.

This paper employs computational approaches to model and explore the efficacy of different incentive structures on the decision making behavior of dispatchers in complex queueing networks, and the subsequent effects of these decisions on teams working within the network and on network performance itself. Computational models that express network structure and function, as well as the decision making process of dispatchers operating within the network and the effect these decisions have on team performance, are presented. Performance of the network under status quo and other incentive structures and decision making processes is illustrated via simulation, validated against data from a large-scale debris removal mission that followed a series of tornadoes in the U.S. state of Alabama in 2011. Results of the simulation experiments suggest that the optimal incentive structure assuming a rational decision maker remains optimal under lower levels of rationality. Furthermore, a simple uniform reward structure is likely to produce performance improvements over the status quo incentive structure under most scenarios.

Recent work on the topic of Interactive Optimization has explored opportunities for exploiting human perceptual and cognitive capabilities within frameworks traditionally associated with mathematical optimization. We flip this perspective in order to consider these same basic issues from a human-centered perspective: that is, we identify the opportunities (and challenges) for exploiting methods associated with mathematical optimization models within a framework of human decision making capabilities, as exemplified by complex, dynamic, and ill-structured problems.The paper examines these issues through the lens of Extreme Event (XE) decision making, where XE are defined as events that are rare and severe and create deep changes in society and are rapidly occurring and must be addressed through careful planning but also ingenuity, with little to no opportunity for revisiting prior decisions. Given this reframing, a human centered taxonomy of opportunities for supporting XE decision making is taken as a starting point. In contrast are cast three different methodological approaches to Interactive Optimization, leading to a discussion of the potential of these approaches to supporting XE decision making. The paper concludes with a discussion of prospects and challenges for future work in this area.

While there may be a tendency to characterize COVID-19 as exclusively a public health issue, engineered structures and services have both mitigated and exacerbated the pandemic's march around the globe, raising questions about the role of engineering in controlling pandemics. Any attempts to answer these questions implicate not only the tools, techniques and problems which we define as within the province of engineering, but also the means by which we arrive at this definition. As described here—in settings ranging from nursing homes to prisons to Brazilian favelas —the COVID-19 crisis has upended a number of foundational notions associated with the practice of hazard mitigation through the design and operation of engineered structures and services. It has revealed the need to examine the conditions and assumptions that characterize the models we construct and the data we collect. We do so through a number of case studies collected during the COVID-19 crisis, leading to implications for the conduct of research and education to support not only further advances in our field but to improved prospects for improved mitigation of pandemics and other hazards.

Objective: This paper investigates factors impacting team performance in the Multi-player Online Battle Arena gaming environment, League of Legends™, by testing an integrated Input Mediator-Outcome team effectiveness framework. Background: Secondary data and Naturally Occurring Data Sets (NODS) are data that have been collected from respondents without research interests in mind and can occur naturally in the environment. There are numerous sources of secondary data, including government data, financial databases, industry association groups, and Application Programming Interfaces, which this research utilizes to study the performance of teams. Methods: Path Analysis and Partial Least Squares Discriminant Analysis (PLS-DA) are analytical methods that are well suited for large data sets and sample sizes, confirmatory in nature, and can test a theoretical model. This research utilizes both in order to study factors impacting team performance. Results: A total of 5,927 matches from 742 teams are sampled and analyzed. Six team performance measures are used to discriminate between winning and losing teams, including role familiarity, team familiarity, team effectiveness, team efficiency, and the Kills, Deaths, Assist (KDA) ratio. Using path analysis and supervised PLS-DA, the models led to the successful prediction of 89.4% of the matches. The error rate for the PLS-DA model is 0.106 (Q2 = 0.523; R2 = 0.551). Conclusions: This work shows how objective, detailed data on teamwork may be used to provide insights into questions of the performance of teams. Additionally, the results demonstrate the value of using path analysis and PLS-DA to test an integrated framework. Application: This research highlights the value and feasibility of studying virtual teams for new insights into team performance.

The study of community resilience—that is, of the ability of a community to anticipate, respond to, and recover from sudden or slow onset shocks—traces its origins to the earliest days of contemporary disaster research. In recent decades, conceptual frameworks—and precise empirical indicators—of community resilience have entered the literature for purposes of reducing disaster vulnerability at the community level. Given the long arc of research on community resilience, however, it is appropriate to address the extent to which contemporary frameworks apply beyond the particular circumstances within which they were created. To this end, this study offers a productive critique of a contemporary framework of community resilience, using data from a community impact survey conducted following the so-called Lisbon earthquake of 1755. This event, the largest earthquake in recorded European history, was the catalyst for a wide range of innovations in disaster management, yet data from the survey have yet to be explored in English-language scholarship. Through the analysis of these data, this study creates a broader historical frame around current conceptualizations of community resilience, simultaneously identifying limitations of current frameworks as well as potential sources of community resilience that lie outside the historical scope of contemporary investigations.

This study surveys and synthesises prior research on the processes by which teams adapt when collaborating on high-tempo, high-stakes work. Of principal concern are adaptation processes within teams operating in extreme environments, whether precipitated by changes within the team itself (such as loss or gain of new members) or within the team's operational environment (such as unplanned-for contingencies). Propositions concerning three aspects of adaptation are developed: changes in the distribution of workflow across team members, improvisation of team members' individual roles, and emergent behaviours intended to compensate for fluctuations in team member performance. Issues and opportunities for addressing these propositions in highly instrumented environments are then identified, taking as a test case the multiplayer online battle arena combat game, League of Legends. This article concludes with a discussion of opportunities and challenges in using detailed and voluminous naturally occurring data from highly instrumented environments - whether real or virtual - to address the propositions presented here.

This research explores relationships between social network structure (as inferred from Twitter posts) and the occurrence of domestic protests following the 2016 US Presidential Election. A hindcasting method is presented which exploits Random Forest classification models to generate predictions about protest occurrence that are then compared to ground truth data. Results show a relationship between social network structure and the occurrence of protests that is stronger or weaker depending on the time frame of prediction.